Commonly, data is accessed and modified in distributed networking examples where one or more users may update content through an application or service. A piece of data that is stored in a distributed storage (e.g., cloud storage) can be updated by multiple users, from multiple devices, and also can be updated through the various services that act on the data. There is a possibility that the data being updated, gets corrupted during update processing. Among other reasons, this may be due to issues on any of the clients or the services that access the data.
Previously, in cases of content corruption, the cause of the issue was identified in an ad-hoc manner where attempts were made to track the issue to a specific client or service that may have accessed a distributed storage. Any errors that were found were fixed. However, this process makes it difficult to consistently identify the cause of the issue as well as identify data in production that may have entered into a corrupted state. Additionally, this type of processing is resource intensive from a processing efficiency standpoint, for example, tying up resources on a client-side as well as a server-side and further requiring additional processing operations to retrieve file data from data storages. A corruption remediation service may be configured to address corruption issues one by one and not recognize that an underlying data structure (for file content) is invalid. Consider an instance where an upload error occurs due to an error in the underlying data structure. Traditional corruption remediation processing may not update the underlying data structure to prevent this issue from occurring, meaning large amounts of data have the potential to enter a corrupted state.